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NEW QUESTION: 1
A learning curve of 80% assumes that the incremental unit time is reduced by 20% for each doubling of output. Also, direct labor cost is proportionate to time worked. What is the incremental direct labor cost of the 16th unit produced as an approximate percentage of the first unit produced?
A. 64%
B. 41%
C. 31%
D. 51%
Answer: B
Explanation:
The assumption is that the incremental unit time (time to produce the last unit) is reduced by 20% when production doubles. Thus, the labor cost of the sixteenth unit is 40.96% of that for the first unit (100% x 80% x 80% x 80% x 80%).
NEW QUESTION: 2
Which of the following best describes the Black-box technique?
A. It ensures all possible branches in the code are tested.
B. It is based on the internal structure of the system.
C. It uses decision coverage for completeness.
D. It can be done without reference to the internal structure of the component or system.
Answer: D
NEW QUESTION: 3
You want to populate an associative array in order to perform a map-side join. You've decided to put this information in a text file, place that file into the DistributedCache and read it in your Mapper before any records are processed.
Indentify which method in the Mapper you should use to implement code for reading the file and populating the associative array?
A. combine
B. configure
C. init
D. map
Answer: B
Explanation:
See 3) below.
Here is an illustrative example on how to use the DistributedCache: // Setting up the cache for the application
1.Copy the requisite files to the FileSystem:
$ bin/hadoop fs -copyFromLocal lookup.dat /myapp/lookup.dat $ bin/hadoop fs -copyFromLocal map.zip /myapp/map.zip $ bin/hadoop fs -copyFromLocal mylib.jar /myapp/mylib.jar $ bin/hadoop fs -copyFromLocal mytar.tar /myapp/mytar.tar $ bin/hadoop fs -copyFromLocal mytgz.tgz /myapp/mytgz.tgz $ bin/hadoop fs -copyFromLocal mytargz.tar.gz /myapp/mytargz.tar.gz
2.Setup the application's JobConf:
JobConf job = new JobConf();
DistributedCache.addCacheFile(new URI("/myapp/lookup.dat#lookup.dat"),
job);
DistributedCache.addCacheArchive(new URI("/myapp/map.zip", job);
DistributedCache.addFileToClassPath(new Path("/myapp/mylib.jar"), job);
DistributedCache.addCacheArchive(new URI("/myapp/mytar.tar", job);
DistributedCache.addCacheArchive(new URI("/myapp/mytgz.tgz", job);
DistributedCache.addCacheArchive(new URI("/myapp/mytargz.tar.gz", job);
3.Use the cached files in the Mapper or Reducer:
public static class MapClass extends MapReduceBase
implements Mapper<K, V, K, V> {
private Path[] localArchives;
private Path[] localFiles;
public void configure(JobConf job) {
// Get the cached archives/files
localArchives = DistributedCache.getLocalCacheArchives(job);
localFiles = DistributedCache.getLocalCacheFiles(job);
}
public void map(K key, V value,
OutputCollector<K, V> output, Reporter reporter)
throws IOException {
// Use data from the cached archives/files here
// ...
// ...
output.collect(k, v);
}
}
Reference: org.apache.hadoop.filecache , Class DistributedCache